NEURAL NETWORK, NEURON, AND METHOD FOR RECOGNIZING A MISSING INPUT VALUE

A neuron (100) has a null-inhibiting function so that null inputs do not affect the output of the neuron (100) or updating of its weights. The neuron (100) provides a net value based on a sum of products of each of several inputs, and corresponding weight and null values, and provides an output in response to the net value. A neural network (40) which uses such a neuron (100) has a first segmented layer (41) in which each segment (50-52) corresponds to a manufacturing process step (60-62). Each segment of the first layer (41) receives as inputs measured values associated with the process step (60-62). A second layer (42) connected to the first layer (4l), is non-segmented to model the entire manufacturing process (80). The first (41) and second (42) layers are both unsupervised and competitive. A third layer (43) connected to the second layer (42) then estimates parameters of the manufacturing process (80) and is unsupervised and noncompetitive.